Phatchakorn Areekul

Notice of Violation of IEEE Publication Principles A Hybrid ARIMA and Neural Network Model for Short-Term Price Forecasting in Deregulated Market

In the framework of competitive electricity markets, power producers and consumers need accurate price forecasting tools. Price forecasts embody crucial information for producers and consumers when planning bidding strategies in order to maximize their benefits and utilities, respectively. The choice of the forecasting model becomes the important influence factor on how to improve price forecasting accuracy. This paper provides a hybrid methodology that combines both autoregressive integrated moving average (ARIMA) and artificial neural network (ANN) models for predicting short-term electricity prices. This method is examined by using the data of Australian national electricity market, New South Wales, in the year 2006. Comparison of forecasting performance with the proposed ARIMA, ANN, and hybrid models are presented. Empirical results indicate that a hybrid ARIMA-ANN model can improve the price forecasting accuracy.

Next day price forecasting in deregulated market by combination of Artificial Neural Network and ARIMA time series models

Electricity price forecasting is becoming increasingly relevant to power producers and consumers in the new competitive electric power markets, when planning bidding strategies in order to maximize their benefits and utilities, respectively. This paper proposed a method to predict hourly electricity prices for next-day electricity markets by combination methodology of ARIMA and ANN models. The proposed method is examined on the Australian National Electricity Market (NEM), New South Wales regional in year 2006. Comparison of forecasting performance with the proposed ARIMA, ANN and combination (ARIMA-ANN) models are presented. Empirical results indicate that an ARIMA-ANN model can improve the price forecasting accuracy.

Combination of artificial neural network and ARIMA time series models for short term price forecasting in deregulated market

In the framework of competitive electricity markets, power producers and consumers need accurate price forecasting tools. Price forecasts embody crucial information for producers and consumers when planning bidding strategies in order to maximize their benefits and utilities, respectively. The choice of the forecasting model becomes the important influence factor how to improve price forecasting accuracy. In this paper provides a combination methodology that combines both ARIMA and ANN models for predicting short term electricity prices. This method is examined by using the data of Australian National Electricity Market (NEM), New South Wales regional in year 2006. Comparison of forecasting performance with the proposed ARIMA and ARIMA-ANN models are presented. Empirical results indicate that an ARIMA-ANN model can improve the price forecasting accuracy.

A new method for next-day price forecasting for PJM electricity market

In the framework of the competitive electricity markets, electricity price forecasting is important for market participants in a deregulated electricity market. Rather than forecasting the value, market participants are sometimes more interested interval of the peak electricity price forecasting. Forecasting the peak price is essential for estimating the uncertainty involved in the price and thus is highly useful for making generation bidding strategies and investment decisions. The choice of the forecasting model becomes the important influence factor how to improve price forecasting accuracy. This paper proposes new approach to reduce the prediction error at occurrence time of the peak electricity price, and aims to enhance the accuracy of the next day electricity price forecasting. In the proposed method, the weekly variation data is used for input factors of the ANN at occurrence time of the peak electricity price in order to catch the price variation. Moreover, learning data for the ANN is selected by rough sets theory at occurrence time of the peak electricity price. This method is examined by using the data of the PJM electricity market. From the simulation results, it is observed that the proposed method provides a more accurate and effective forecasting, which helpful for suitable bidding strategy and risk management tool for market participants in a deregulated electricity market.

Next-day electricity price forecasting on deregulated power market

This paper proposes the approach to reduce the prediction error at occurrence time of peak electricity price, and aims to enhance the accuracy of next day electricity price forecasting. In the proposed method, the weekly variation data is used for input factors of the NN at occurrence time of peak electricity price in order to catch the price variation. Moreover, learning data for the neural network (NN) is selected by rough sets theory at occurrence time of peak electricity price. This method is examined by using the data of PJM electricity market.

Neural-wavelet Approach for Short Term Price Forecasting in Deregulated Power Market

This paper proposes designing a model using artificial neural network (ANN) and wavelet techniques to increase the accuracy of short term price forecast in the electricity market. The prior electricity price data are treated as time series. They are decomposed into several wavelet coefficient series using the wavelet transform technique known as Discrete Wavelet Transform (DWT), while the forecast model is based on wavelet multi-resolution (MR) decomposition. The wavelet coefficient series are used to train the artificial neural network and used as the inputs to the ANN for electricity price prediction. The Scale Conjugate Gradient (SCG) algorithm is used as the learning algorithm for the ANN. To get the final forecast data, the outputs from the ANN are recombined using the same wavelet technique. The model was evaluated with electricity price data of New South Wales Australia for the year 2008. Empirical results indicate that the WT-ANN combination model improves the price forecasting accuracy.

Next day price forecasting for electricity market

This paper proposes new approach to reduce the prediction error at occurrence time of the peak price, and aims to enhance the accuracy of the next day price forecasting. In the proposed method, the weekly variation data is used for input factors of the ANN at occurrence time of the peak price in order to catch the price variation. Moreover, learning data for the ANN is selected by rough sets theory at occurrence time of the peak price. From the simulation results, it is observed that the proposed method provides a more accurate and effective forecasting, which helpful for suitable bidding strategy and risk management tool for market participants in a deregulated electricity market.